Here I post projects with something interesting and useful.
Each project is a .ipynb file with:
- formally set task
- EDA
- working code
- text explanations
- visualized results
- conclusions and / or plans for improving the result
| Project | Methods | Stack |
|---|---|---|
| Full face recognition pipeline | Face detection (YOLO), face landmarks coordinates regression, face alignment, face embedding DNN, cos similarity analysis, telegram bot implementation | python, pytorch, albumentations, timm, aiogram, cv2 |
| DCGAN for face generation | Hand-made DCGAN, custom Gauss noise layers, gradien penalty, regularization by labels (soft, noise, flip), specific metrics: Fréchet inception distance и Leave-one-out-1-NN classification, epoch animation | python, pytorch, matplotlib, albumentations, timm |
| Project | Methods | Stack |
|---|---|---|
| Person Age by Photo Yandex Practicum |
EffecientNet V2 pretrained, strong augmentation, result analysis | python, pytorch, matplotlib, albumentations, timm, альтернативный код в keras |
| Image Autoencoders DL school MIPT |
CNN, latent vectors, latent space sampling, VAE, Conditional VAE, KL divergence loss | python, pytorch, sklearn, matplotlib |
| Toxic Comment Classification Yandex Practicum |
Text preprocessing, TF-IDF, lemmatization, word2vec, fine-tuned BERT, weighted loss | python, pandas, numpy, pytorch, BERT, sklearn, optuna, matplotlib, seaborn, wordcloud, nltk, enchant, spacy, gensim |
| Semantic Segmentation of Skin Lesions DL school MIPT |
CNN, hand-made Unet и SegNet (based on VGG16), image augmentations, custom loss functions | python, pytorch, sklearn, albumentations, matplotlib |
| Gold Recovery Efficiency Model Yandex Practicum |
EDA, preprocessing, feature selection by phik correlation, feature dimensionality reduction, gradient boosting, hyperparameter tuning | python, pandas, seaborn, sklearn, pipeline, optuna, phik, xgboost |
| Simpsons Classification DL school MIPT |
CNN, transfer learning, image augmentation, class imbalance, extended result visualization | python, pytorch, sklearn, matplotlib |
| Taxi Demand Time Series Yandex Practicum |
Exponential smoothing, SARIMA, linear regression, gradient boosting, hybrid boosting, hyperparameter tuning, neural networks: dense, LSTM | python, pandas, numpy, seaborn, sklearn, pipeline, statsmodels, pytorch, lightgbm, optuna, prophet, phik, pmdarima |
| Used Car Price Prediction Yandex Practicum |
EDA, deep preprocessing, feature selection, gradient boosting, hyperparameter tuning, fully connected neural network, residual analysis, postcode coordinates, feature importance (permutation) | python, pandas, numpy, seaborn, sklearn, pipeline, pytorch, lightgbm, xgboost, catboost, optuna, phik |
| Bank Customer Churn Yandex Practicum |
Class imbalance, T-SNE visualization, gradient boosting, hyperparameter tuning, cross-validation ROC curves, feature engineering, wrapper class, stacking, feature importance (permutation) | python, pandas, numpy, seaborn, sklearn, pipeline, lightgbm, xgboost, optuna, |
| Titanic Analysis Kaggle |
Advanced analysis, advanced visualization, gradient boosting, hyperparameter tuning | python, pandas, numpy, seaborn, plotly, sklearn, catboost, optuna, |
| Selecting Oil Well Location Yandex Practicum |
Synthetic data, bootstrap, QQ-plot, confidence intervals | python, pandas, numpy, seaborn, sklearn, pipeline |
| Video Game Market Analysis Yandex Practicum |
Deep data analysis, advanced visualization, hypothesis testing | python, pandas, numpy, seaborn, plotly, scipy |
| Tariff Recommendation for a Mobile Operator Client Yandex Practicum |
Classic ML models: logistic regression with feature engineering, SVM, naive Bayes classifier, decision trees, random forest, gradient boosting, model stacking | python, pandas, sklearn, numpy, seaborn, xgboost |
E-mail: sergey.troschiev@gmail.com
Telegram: https://t.me/sergey_doc